{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font size=6>决策树算法</font>\n",
    "# 决策树概述\n",
    "决策树（Decision Tree）算法是一种基本的分类与回归方法，是最经常使用的数据挖掘算法之一。决策树模型呈树形结构，在分类问题中，表示基于特征对实例进行分类的过程。它可以认为是 if-then 规则的集合，也可以认为是定义在特征空间与类空间上的条件概率分布。\n",
    "\n",
    "决策树学习通常包括 3 个步骤：特征选择、决策树的生成和决策树的修剪。\n",
    "<img src=\"images/162042244705421.jpg\">\n",
    "\n",
    "决策树由结点（node）和有向边（directed edge）组成。结点有两种类型：内部结点（internal node）和叶结点（leaf node）。内部结点表示一个特征或属性(features)，叶结点表示一个类(labels)。\n",
    "\n",
    "用决策树对需要测试的实例进行分类的流程是：\n",
    "\n",
    "从根节点开始，对实例的某一特征进行测试，根据测试结果，将实例分配到其子结点；这时，每一个子结点对应着该特征的一个取值。如此递归地对实例进行测试并分配，直至达到叶结点。最后将实例分配到叶结点的类中。\n",
    "# 决策树原理\n",
    "## 熵（entropy）\n",
    "熵指的是体系的混乱的程度，在不同的学科中也有引申出的更为具体的定义，是各领域十分重要的参量。信息论（information theory）中的熵（香农熵）： 是一种信息的度量方式，表示信息的混乱程度，也就是说：信息越有序，信息熵越低。例如：火柴有序放在火柴盒里，熵值很低，相反，熵值很高。具体的，随机变量$X$的熵的表示式如下：\n",
    "$$H(X)=-\\sum_{i=1}^n{p_i}\\log p_i$$\n",
    "\n",
    "其中，$n$代表$X$的$n$种不同的离散取值。而$p_i$代表了$X$取值为$i$的概率，$\\log$为以2或者$e$为底的对数。**概率分布越均匀，熵值越大。**\n",
    "## 联合熵\n",
    "两个变量$X和Y$的联合熵表达式：\n",
    "$$H(X,Y)=-\\sum_{i=1}^n{p(x_i,y_i)\\log p(x_i,y_i)}$$\n",
    "## 条件熵\n",
    "$$H(X|Y)=-\\sum_{i=1}^n{p(x_i|y_i)\\log p(x_i,y_i)}=\\sum_{i=1}^n{p(y_i)H(X|y_i)}$$\n",
    "## 信息增益\n",
    "熵$H(X)$度量了$X$的不确定性，条件熵$H(X|Y)$度量了条件$Y$下$X$的不确定性。那么：\n",
    "$$I(X,Y)=H(X)-H(X|Y)$$\n",
    "称为，信息增益也叫做互信息，它度量了条件$Y$下$X$不确定性减少的程度。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 决策树ID3算法\n",
    "ID3算法就是用信息增益大小来判断当前节点应该用什么特征来构建决策树，即，用计算出的信息增益最大的特征来建立决策树的当前节点。\n",
    "\n",
    "## 案例\n",
    "第一列为用户id，第二列为性别，第三列为活跃度，最后一列用户是否流失。我们要解决一个问题：性别和活跃度两个特征，哪个对用户流失影响更大？我们通过计算信息熵可以解决这个问题。\n",
    "<table><tr>\n",
    "    <td><img src=\"images/1491038386555_664_1491038386638.jpg\"></td>\n",
    "    <td><img src=\"images/1491038414908_7639_1491038414978.jpg\"></td>\n",
    "    </tr></table>\n",
    "    \n",
    "**整体熵：**\n",
    "<!--$$H(X)=-\\frac{5}{15}\\log_2{(\\frac{5}{15})}-\\frac{10}{15}\\log_2{(\\frac{10}{15})}$$-->"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from math import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def h(*args):\n",
    "    r=0\n",
    "    s=sum(args)\n",
    "    args=[i/s for i in args]\n",
    "    for i in args:\n",
    "        if i!=0 and i!=1:\n",
    "            r+=-i*log2(i)\n",
    "    return r"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9182958340544896"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hx=h(5,10)\n",
    "hx"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**性别熵：**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.954434002924965, 0.863120568566631)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hg1,hg2=h(3,5),h(2,5)\n",
    "hg1,hg2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**性别信息增益**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.006474767163413719"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ig=hx-8/15*hg1-7/15*hg2\n",
    "ig"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**活跃度增益：**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6776531357587021"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ia=hx-6/15*h(0,6)-5/15*h(1,4)-4/15*h(4,0)\n",
    "ia"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "活跃度的信息增益比性别的信息增益大，也就是说，活跃度对用户流失的影响比性别大。在做特征选择或者数据分析的时候，我们应该重点考察活跃度这个指标。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 算法流程\n",
    "输入的是$m$个样本，样本输出集合为$D$，每个样本有$n$个离散特征，特征集合即为$A$，输出为决策树$T$。\n",
    "\n",
    "**流程：**\n",
    "1. 初始化信息增益的阈值$ϵ$\n",
    "2. 判断样本是否为同一类输出$D_i$，如果是则返回单节点树$T$。标记类别为$D_i$\n",
    "3. 判断特征是否为空，如果是则返回单节点树$T$，标记类别为样本中输出类别$D$实例数最多的类别。\n",
    "4. 计算$A$中的各个特征（一共$n$个）对输出$D$的信息增益，选择信息增益最大的特征$A_g$\n",
    "5. 如果$A_g$的信息增益小于阈值$ϵ$，则返回单节点树$T$，标记类别为样本中输出类别$D$实例数最多的类别\n",
    "6. 否则，按特征$A_g$的不同取值$A_{gi}$将对应的样本输出$D$分成不同的类别$D_i$。每个类别产生一个子节点。对应特征值为$A_{gi}$。返回增加了节点的树$T$\n",
    "7. 对于所有的子节点，令$D=D_i,A=A−\\{A_g\\}$递归调用2-6步，得到子树$T_i$并返回。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 决策树ID3算法的不足\n",
    "ID3算法虽然提出了新思路，但是还是有很多值得改进的地方。　　\n",
    "1. ID3没有考虑连续特征，比如长度，密度都是连续值，无法在ID3运用。这大大限制了ID3的用途。\n",
    "1. ID3采用信息增益大的特征优先建立决策树的节点。很快就被人发现，在相同条件下，取值比较多的特征比取值少的特征信息增益大。比如一个变量有2个值，各为1/2，另一个变量为3个值，各为1/3，其实他们都是完全不确定的变量，但是取3个值的比取2个值的信息增益大。如果校正这个问题呢？\n",
    "1.  ID3算法对于缺失值的情况没有做考虑\n",
    "1.  没有考虑过拟合的问题\n",
    "\n",
    "ID3算法的作者对ID3算法做了改进，这就是C4.5算法。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 决策树C4.5算法\n",
    "C4.5算法对于ID3的上述不足做了改进。\n",
    "## 改进\"不能处理连续特征\"\n",
    "C4.5的思路是将连续的特征离散化。比如$m$个样本的连续特征$A$：\n",
    "1. 从小到大排列为$a_1,a_2,\\dots,a_m$,取相邻两样本值的平均数，一共取得$m-1$个划分点，其中第$i$个划分点$T_i$表示为：$Ti=\\frac{a_i+a_{i+1}}{2}$。\n",
    "2. 对于这$m-1$个点，分别计算以该点作为二元分类点时的信息增益。选择信息增益最大的点作为该连续特征的二元离散分类点。比如取到的增益最大的点为$a_t$,则小于$a_t$的值为类别1，大于$a_t$的值为类别2，这样我们就做到了连续特征的离散化。\n",
    "\n",
    "要注意的是，与离散属性不同的是，如果当前节点为连续属性，则该属性后面可以参与子节点的产生选择过程。\n",
    "## 改进“信息增益作为标准容易偏向于取值较多的特征的问题”\n",
    "我们引入一个信息增益比的变量$I_R(X,Y)$，它是信息增益和特征熵的比值。表达式如下：\n",
    "$$I_R(X,Y)=\\frac{I(A,D)}{H_A(D)}$$\n",
    "\n",
    "其中$D$为样本特征输出的集合，$A$为样本特征，对于特征熵$H_A(D)$, 表达式如下：\n",
    "\n",
    "$$H_A(D)=-\\sum_{i=1}^n{\\frac{|D_i|}{|D|}\\log_2\\frac{|D_i|}{|D|}}$$\n",
    "\n",
    "其中$n$为特征$A$的类别数， $D_i$为特征$A$的第$i$个取值对应的样本个数。$|D|$为样本个数。\n",
    "\n",
    "特征数越多的特征对应的特征熵越大，它作为分母，可以校正信息增益容易偏向于取值较多的特征的问题。\n",
    "## 改进“缺失值处理的问题”\n",
    "需要解决的是两个问题，一是在样本某些特征缺失的情况下选择划分的属性，二是选定了划分属性，对于在该属性上缺失特征的样本的处理。\n",
    "\n",
    "**对于第一个子问题：**\n",
    "\n",
    "对于某一个有缺失特征值的特征$A$。C4.5的思路是将数据分成两部分，对每个样本设置一个权重（初始可以都为1），然后划分数据，一部分是有特征值$A$的数据$D_1$，另一部分是没有特征A的数据$D_2$. 然后对于没有缺失特征$A$的数据集$D_1$来和对应的$A$特征的各个特征值一起计算加权重后的信息增益比，最后乘上一个系数，这个系数是无特征$A$缺失的样本加权后所占加权总样本的比例。\n",
    "\n",
    "**对于第二个子问题：**\n",
    "\n",
    "可以将缺失特征的样本同时划分入所有的子节点，不过将该样本的权重按各个子节点样本的数量比例来分配。\n",
    "## 决策树C4.5算法的不足与思考\n",
    "C4.5虽然改进或者改善了ID3算法的几个主要的问题，仍然有优化的空间。\n",
    "1. 由于决策树算法非常容易过拟合，因此对于生成的决策树必须要进行剪枝。剪枝的算法有非常多，C4.5的剪枝方法有优化的空间。思路主要是两种:\n",
    "    1. 一种是预剪枝，即在生成决策树的时候就决定是否剪枝。\n",
    "    2. 另一个是后剪枝，即先生成决策树，再通过交叉验证来剪枝。\n",
    "2. C4.5生成的是多叉树，即一个父节点可以有多个节点。很多时候，在计算机中二叉树模型会比多叉树运算效率高。如果采用二叉树，可以提高效率。\n",
    "3. C4.5只能用于分类，如果能将决策树用于回归的话可以扩大它的使用范围。\n",
    "4. C4.5由于使用了熵模型，里面有大量的耗时的对数运算,如果是连续值还有大量的排序运算。如果能够加以模型简化可以减少运算强度但又不牺牲太多准确性的话，那就更好了。\n",
    "\n",
    "这4个问题在CART树里面部分加以了改进。所以目前如果不考虑集成学习话，在普通的决策树算法里，CART算法算是比较优的算法了。scikit-learn的决策树使用的也是CART算法。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# CART分类树算法\n",
    "## CART分类树算法的最优特征选择方法\n",
    "CART分类树算法使用基尼系数来代替信息增益比，基尼系数代表了模型的不纯度，基尼系数越小，则不纯度越低，特征越好。这和信息增益(比)是相反的。\n",
    "\n",
    "具体的，在分类问题中，假设有$K$个类别，第$k$个类别的概率为$p_k$, 则基尼系数的表达式为：\n",
    "$$Gini(p)=\\sum_{k=1}^K{p_k(1-p_k)}=1-\\sum_{k=1}^K{p_k^2}$$\n",
    "\n",
    "对于个给定的样本$D$,假设有$K$个类别, 第$k$个类别的数量为$C_k$,则样本$D$的基尼系数表达式为：\n",
    "$$Gini(D)=1-\\sum_{k=1}^K{(\\frac{|C_k|}{|D|})}^2$$\n",
    "\n",
    "特别的，对于样本$D$,如果根据特征$A$的某个值$a$,把$D$分成$D_1$和$D_2$两部分，则在特征$A$的条件下，$D$的基尼系数表达式为：\n",
    "$$Gini(D,A)=\\frac{|D_1|}{|D|}Gini(D_1)+\\frac{|D_2|}{|D|}Gini(D_2)$$\n",
    "## CART分类树算法对于连续特征和离散特征处理的改进\n",
    "对于CART分类树连续值的处理问题，其思想和C4.5是相同的，都是将连续的特征离散化。唯一的区别在于在选择划分点时的度量方式不同，C4.5使用的是信息增益比，则CART分类树使用的是基尼系数。\n",
    "\n",
    "CART分类树离散值的处理，采用的思路是不停的二分离散特征。\n",
    "\n",
    "回忆下ID3或者C4.5，如果某个特征A被选取建立决策树节点，如果它有A1,A2,A3三种类别，我们会在决策树上一下建立一个三叉的节点。这样导致决策树是多叉树。但是CART分类树使用的方法不同，他采用的是不停的二分，CART分类树会考虑把A分成{A1}和{A2,A3}, {A2}和{A1,A3}, {A3}和{A1,A2}三种情况，找到基尼系数最小的组合，比如{A2}和{A1,A3},然后建立二叉树节点，一个节点是A2对应的样本，另一个节点是{A1,A3}对应的节点。同时，由于这次没有把特征A的取值完全分开，后面我们还有机会在子节点继续选择到特征A来划分A1和A3。这和ID3或者C4.5不同，在ID3或者C4.5的一棵子树中，离散特征只会参与一次节点的建立。\n",
    "\n",
    "## CART分类树建立算法的具体流程\n",
    "算法输入是训练集D，基尼系数的阈值，样本个数阈值。输出是决策树T。\n",
    "\n",
    "我们的算法从根节点开始，用训练集递归的建立CART树。\n",
    "1. 对于当前节点的数据集为$D$，如果样本个数小于阈值或者没有特征，则返回决策子树，当前节点停止递归。\n",
    "2. 计算样本集$D$的基尼系数，如果基尼系数小于阈值，则返回决策树子树，当前节点停止递归。\n",
    "3. 计算当前节点现有的各个特征的各个特征值对数据集$D$的基尼系数。\n",
    "4. 在计算出来的各个特征值对数据集$D$的基尼系数中，选择基尼系数最小的特征$A$和对应的特征值$a$。根据这个最优特征和最优特征值，把数据集划分成两部分$D1$和$D2$，同时建立当前节点的左右节点，左节点的数据集$D为D1$，右节点的数据集$D为D2$.\n",
    "5. 对左右的子节点递归的调用1-4步，生成决策树。\n",
    "\n",
    "对于生成的决策树做预测的时候，假如测试集里的样本A落到了某个叶子节点，而节点里有多个训练样本。则对于A的类别预测采用的是这个叶子节点里概率最大的类别。\n",
    "\n",
    "## CART回归树建立算法\n",
    "CART回归树和CART分类树的建立和预测的区别主要有下面两点：\n",
    "1. 连续值的处理方法不同\n",
    "2. 决策树建立后做预测的方式不同。\n",
    "\n",
    "**对于连续值的处理：**\n",
    "\n",
    "回归树，使用了常见的和方差的度量方式，CART回归树的度量目标是，对于任意划分特征$A$，对应的任意划分点$s$两边划分成的数据集$D1和D2$，求出使$D1和D2$各自集合的均方差最小，以及对应的特征和特征值划分点。表达式为：\n",
    "$$\\large\\underbrace{min}_{A,s}\\Bigg[\\underbrace{min}_{c_1}\\sum\\limits_{x_i \\in D_1(A,s)}(y_i - c_1)^2 + \\underbrace{min}_{c_2}\\sum\\limits_{x_i \\in D_2(A,s)}(y_i - c_2)^2\\Bigg]$$\n",
    "\n",
    "对于决策树建立后做预测的方式，上面讲到了CART分类树采用叶子节点里概率最大的类别作为当前节点的预测类别。而回归树输出不是类别，它采用的是用最终叶子的均值或者中位数来预测输出结果。\n",
    "## CART树算法的剪枝\n",
    "由于决策时算法很容易对训练集过拟合，而导致泛化能力差，为了解决这个问题，我们需要对CART树进行剪枝，即类似于线性回归的正则化，来增加决策树的泛化能力。\n",
    "\n",
    "CART树的剪枝算法可以概括为两步:\n",
    "1. 第一步是从原始决策树生成各种剪枝效果的决策树，\n",
    "2. 第二部是用交叉验证来检验剪枝后的预测能力，选择泛化预测能力最好的剪枝后的数作为最终的CART树。\n",
    "\n",
    "### 剪枝的损失函数度量\n",
    "在剪枝的过程中，对于任意的一颗子树T,其损失函数为：\n",
    "$$C_{\\alpha}(T_t) = C(T_t) + \\alpha |T_t|$$\n",
    "\n",
    "其中，$α$为正则化参数，这和线性回归的正则化一样。$C(T_t)$为训练数据的预测误差，分类树是用基尼系数度量，回归树是均方差度量。$|T_t|$是子树T的叶子节点的数量。\n",
    "\n",
    "当$α=0$时，即没有正则化，原始的生成的CART树即为最优子树。当$α=∞$时，即正则化强度达到最大，此时由原始的生成的CART树的根节点组成的单节点树为最优子树。当然，这是两种极端情况。一般来说，$α$越大，则剪枝剪的越厉害，生成的最优子树相比原生决策树就越偏小。对于固定的$α$，一定存在使损失函数$C_α(T)$最小的唯一子树。\n",
    "### 剪枝的思路\n",
    "对于位于节点$t$的任意一颗子树$T_t$，如果没有剪枝，它的损失是:\n",
    "$$C_{\\alpha}(T_t) = C(T_t) + \\alpha |T_t|$$\n",
    "\n",
    "如果将其剪掉，仅仅保留根节点，则损失是:\n",
    "$$C_{\\alpha}(T) = C(T) + \\alpha$$\n",
    "\n",
    "当$α=0$或者$α$很小时，$$C_{\\alpha}(T_t) < C_{\\alpha}(T)$$\n",
    "\n",
    "当$α$增大到一定的程度时\n",
    "$$C_{\\alpha}(T_t) = C_{\\alpha}(T)$$\n",
    "\n",
    "当$α$继续增大时不等式反向，也就是说，如果满足下式：\n",
    "$$\\alpha = \\frac{C(T)-C(T_t)}{|T_t|-1}$$\n",
    "\n",
    "$T_t和T$有相同的损失函数，但是$T$节点更少，因此可以对子树$T_t$进行剪枝，也就是将它的子节点全部剪掉，变为一个叶子节点$T$。\n",
    "### CART树的交叉验证策略\n",
    "可以计算出每个子树是否剪枝的阈值$α$，如果我们把所有的节点是否剪枝的值$α$都计算出来，然后分别针对不同的$α$所对应的剪枝后的最优子树做交叉验证。这样就可以选择一个最好的$α$，有了这个$α$，我们就可以用对应的最优子树作为最终结果。\n",
    "\n",
    "### CART树的剪枝算法\n",
    "输入是CART树建立算法得到的原始决策树$T$。输出是最优决策子树$T_α$。\n",
    "\n",
    "算法过程如下：\n",
    "1. 初始化$\\alpha_{min}= \\infty$， 最优子树集合$\\omega=\\{T\\}$。\n",
    "2. 从叶子节点开始自下而上计算各内部节点t的训练误差损失函数$C_{\\alpha}(T_t)$（回归树为均方差，分类树为基尼系数）, 叶子节点数$|T_t|$，以及正则化阈值$\\alpha= min\\{\\frac{C(T)-C(T_t)}{|T_t|-1}, \\alpha_{min}\\}$, 更新$α_{min}=α$\n",
    "3. 得到所有节点的$α$值的集合$M$。\n",
    "4. 从$M$中选择最大的值$α_k$，自上而下的访问子树$t$的内部节点，如果$\\frac{C(T)-C(T_t)}{|T_t|-1} \\leq \\alpha_k$时，进行剪枝。并决定叶节点t的值。如果是分类树，则是概率最高的类别，如果是回归树，则是所有样本输出的均值。这样得到$α_k$对应的最优子树$T_k$\n",
    "5. 最优子树集合$\\omega=\\omega \\cup T_k$，$M= M -\\{\\alpha_k\\}$。\n",
    "6. 如果$M$不为空，则回到步骤4。否则就已经得到了所有的可选最优子树集合$ω$.\n",
    "7. 采用交叉验证在$ω$选择最优子树$T_α$\n",
    "\n",
    "## CART算法小结\n",
    "<table style=\"height: 88px; width: 100%;\" border=\"1\">\n",
    "<tbody>\n",
    "<tr>\n",
    "<td>算法</td>\n",
    "<td>支持模型</td>\n",
    "<td>树结构</td>\n",
    "<td>特征选择</td>\n",
    "<td>连续值处理</td>\n",
    "<td>缺失值处理</td>\n",
    "<td>&nbsp;剪枝</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>ID3</td>\n",
    "<td>分类</td>\n",
    "<td>多叉树</td>\n",
    "<td>信息增益</td>\n",
    "<td>不支持</td>\n",
    "<td>&nbsp;不支持</td>\n",
    "<td>&nbsp;不支持</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>C4.5</td>\n",
    "<td>分类</td>\n",
    "<td>多叉树</td>\n",
    "<td>信息增益比</td>\n",
    "<td>支持</td>\n",
    "<td>&nbsp;支持</td>\n",
    "<td>&nbsp;支持</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>CART</td>\n",
    "<td>分类，回归</td>\n",
    "<td>二叉树</td>\n",
    "<td>基尼系数，均方差</td>\n",
    "<td>支持</td>\n",
    "<td>&nbsp;支持</td>\n",
    "<td>&nbsp;支持</td>\n",
    "</tr>\n",
    "</tbody>\n",
    "</table>\n",
    "\n",
    "**CART算法主要的缺点:**\n",
    "1. 无论是ID3, C4.5还是CART,在做特征选择的时候都是选择最优的一个特征来做分类决策，但是大多数，分类决策不应该是由某一个特征决定的，而是应该由一组特征决定的。这样决策得到的决策树更加准确。这个决策树叫做多变量决策树(multi-variate decision tree)。在选择最优特征的时候，多变量决策树不是选择某一个最优特征，而是选择最优的一个特征线性组合来做决策。\n",
    "2. 如果样本发生一点点的改动，就会导致树结构的剧烈改变。这个可以通过集成学习里面的随机森林之类的方法解决。　"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 决策树算法优缺点\n",
    "优点：\n",
    "\n",
    "1. 简单直观，生成的决策树很直观。\n",
    "2. 基本不需要预处理，不需要提前归一化，处理缺失值。\n",
    "3. 使用决策树预测的代价是$O(\\log_2^m)$。 m为样本数。\n",
    "4. 既可以处理离散值也可以处理连续值。很多算法只是专注于离散值或者连续值。\n",
    "5. 可以处理多维度输出的分类问题。\n",
    "6. 相比于神经网络之类的黑盒分类模型，决策树在逻辑上可以得到很好的解释\n",
    "7. 可以交叉验证的剪枝来选择模型，从而提高泛化能力。\n",
    "8. 对于异常点的容错能力好，健壮性高。\n",
    "\n",
    "缺点:\n",
    "\n",
    "1. 决策树算法非常容易过拟合，导致泛化能力不强。可以通过设置节点最少样本数量和限制决策树深度来改进。\n",
    "2. 决策树会因为样本发生一点点的改动，就会导致树结构的剧烈改变。这个可以通过集成学习之类的方法解决。\n",
    "3. 寻找最优的决策树是一个NP难的问题，我们一般是通过启发式方法，容易陷入局部最优。可以通过集成学习之类的方法来改善。\n",
    "4. 有些比较复杂的关系，决策树很难学习，比如异或。一般这种情况可以换神经网络分类方法来解决。\n",
    "5. 如果某些特征的样本比例过大，生成决策树容易偏向于这些特征。这个可以通过调节样本权重来改善。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# scikit-learn决策树算法类库\n",
    "scikit-learn决策树算法类库内部实现是使用了调优过的CART树算法，既可以做分类，又可以做回归。分类决策树的类对应的是DecisionTreeClassifier，而回归决策树的类对应的是DecisionTreeRegressor。两者的参数定义几乎完全相同，但是意义不全相同。\n",
    "## DecisionTreeClassifier和DecisionTreeRegressor 重要参数调参注意点\n",
    "1. criterion(特征选择标准)\n",
    "2. splitter(特征划分点选择标准)\n",
    "3. max_features(划分时考虑的最大特征数)\n",
    "4. max_depth(决策树最大深)\n",
    "5. min_samples_split(内部节点再划分所需最小样本数)\n",
    "6. min_samples_leaf(叶子节点最少样本数)\n",
    "7. min_weight_fraction_leaf(叶子节点最小的样本权重和)\n",
    "8. max_leaf_nodes(最大叶子节点数)\n",
    "9. class_weight(类别权重)\n",
    "10. min_impurity_split(节点划分最小不纯度)\n",
    "11. presort(数据是否预排序)\n",
    "\n",
    "除了这些参数以外，其他在调参时的注意点有：\n",
    "1. 当样本数量少但是样本特征非常多的时候，决策树很容易过拟合，一般来说，样本数比特征数多一些会比较容易建立健壮的模型\n",
    "2. 如果样本数量少但是样本特征非常多，在拟合决策树模型前，先做维度规约，比如主成分分析（PCA），特征选择（Losso）或者独立成分分析（ICA）。这样特征的维度会大大减小。再来拟合决策树模型效果会好。\n",
    "3. 推荐多用决策树的可视化，同时先限制决策树的深度（比如最多3层），这样可以先观察下生成的决策树里数据的初步拟合情况，然后再决定是否要增加深度。\n",
    "4. 在训练模型先，注意观察样本的类别情况（主要指分类树），如果类别分布非常不均匀，就要考虑用class_weight来限制模型过于偏向样本多的类别。\n",
    "5. 决策树的数组使用的是numpy的float32类型，如果训练数据不是这样的格式，算法会先做copy再运行。\n",
    "6. 如果输入的样本矩阵是稀疏的，推荐在拟合前调用csc_matrix稀疏化，在预测前调用csr_matrix稀疏化。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## scikit-learn决策树可视化环境搭建\n",
    "1. 第一步是安装graphviz。下载地址在：http://www.graphviz.org/.\n",
    "如果你是linux，可以用apt-get或者yum的方法安装。如果是windows，就在官网下载msi文件安装。无论是linux还是windows，装完后都要设置环境变量，将graphviz的bin目录加到PATH，比如我是windows，将C:/Program Files (x86)/Graphviz2.38/bin/加入了PATH\n",
    "2. 第二步是安装python插件graphviz： pip install graphviz\n",
    "3. 第三步是安装python插件pydotplus。这个没有什么好说的: pip install pydotplus\n",
    "4. 使用时，在代码里面加入这一行：\n",
    "    os.environ[\"PATH\"] += os.pathsep + 'C:/Program Files (x86)/Graphviz2.38/bin/'，注意后面的路径是你自己的graphviz的bin目录。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## DecisionTreeClassifier实例"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from itertools import product\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "from sklearn import datasets\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "# 使用自带的iris数据\n",
    "iris = datasets.load_iris()\n",
    "s=[0, 2]\n",
    "X = iris.data[:, s]\n",
    "y = iris.target\n",
    "\n",
    "# 训练模型，限制树的最大深度4\n",
    "clf = DecisionTreeClassifier(max_depth=4)\n",
    "#拟合模型\n",
    "clf.fit(X, y)\n",
    "\n",
    "# 画图\n",
    "x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1\n",
    "y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1\n",
    "xx, yy = np.meshgrid(np.arange(x_min, x_max, 0.1),\n",
    "                     np.arange(y_min, y_max, 0.1))\n",
    "\n",
    "Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])\n",
    "Z = Z.reshape(xx.shape)\n",
    "\n",
    "plt.contourf(xx, yy, Z, alpha=0.4)\n",
    "plt.scatter(X[:, 0], X[:, 1], c=y, alpha=0.8)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Image  \n",
    "from sklearn import tree\n",
    "import pydotplus \n",
    "import os \n",
    "os.environ[\"PATH\"] += os.pathsep + 'C:/Program Files (x86)/Graphviz2.38/bin/'\n",
    "dot_data = tree.export_graphviz(clf, out_file=None, \n",
    "                         feature_names=[iris.feature_names[i] for i in s],\n",
    "                         class_names=iris.target_names,  \n",
    "                         filled=True, rounded=True,  \n",
    "                         special_characters=True)  \n",
    "graph = pydotplus.graph_from_dot_data(dot_data)  \n",
    "Image(graph.create_png())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  },
  "varInspector": {
   "cols": {
    "lenName": 16,
    "lenType": 16,
    "lenVar": 40
   },
   "kernels_config": {
    "python": {
     "delete_cmd_postfix": "",
     "delete_cmd_prefix": "del ",
     "library": "var_list.py",
     "varRefreshCmd": "print(var_dic_list())"
    },
    "r": {
     "delete_cmd_postfix": ") ",
     "delete_cmd_prefix": "rm(",
     "library": "var_list.r",
     "varRefreshCmd": "cat(var_dic_list()) "
    }
   },
   "types_to_exclude": [
    "module",
    "function",
    "builtin_function_or_method",
    "instance",
    "_Feature"
   ],
   "window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
